Semi-Automated Quality Assurance in Digital Pathology: Tile Classification Approach

Quality assurance is a critical but underexplored area in digital pathology, where even minor artifacts can have significant effects. Artifacts have been shown to negatively impact the performance of AI diagnostic models. In current practice, trained staff manually review digitized images prior to release of these slides to pathologists which are then used to render a diagnosis. Conventional image processing approaches, provide a foundation for detecting artifacts on digital pathology slides. However, current tools do not leverage deep learning, which has the potential to improve detection accuracy and scalability. Despite these advancements, methods for quality assurance in digital pathology remain limited, presenting a gap for innovation. We propose an AI algorithm designed to screen digital pathology slides by analyzing tiles and categorizing them into one of 10 predefined artifact types or as background. This algorithm identifies and localizes artifacts, creating a map that highlights regions of interest. By directing human operators to specific tiles affected by artifacts, the algorithm minimizes the time and effort required to manually review entire slides for quality issues. From internal archives and The Cancer Genome Atlas, 133 whole slide images were selected and 10 artifacts were annotated using an internally developed software ZAPP (Mayo Clinic, Jacksonville, FL). Ablation study of multiple models at different tile sizes and magnification was performed. InceptionResNet was selected. Single artifact models were trained and tested, followed by a limited multiple instance model with artifacts that performed well together (chatter, fold, and pen). From the results of this study we suggest a hybrid design for artifact screening composed of both single artifact binary models as well as multiple instance models to optimize detection of each artifact.

Meta-learning based Selective Fixed-filter Active Noise Control System with ResNet Classifier

The selective fixed-filter strategy is popular in industrial applications involving active noise control (ANC) technology, which circumvents the time-consuming online learning process by selecting the best-matched pre-trained control filter. However, the existing selective fixed-filter ANC (SFANC) based algorithms classify noises in frequency band, which is not a reasonable approach. Moreover, they pre-train the control filter utilizing only a single noise segment, leading to inaccurate estimation and undesirable noise cancellation performance when dealing with dynamically time-varying noise. Inspired by the applicability of meta-learning to various models utilizing gradient descent technique, this paper proposes a novel meta-learning based SFANC system, wherein the fixed-filters that may not be optimal for specific types of noises but can rapidly adapt to previously unseen noise conditions are pre-trained. To address the mismatch issue between meta-learning update methods and ANC requirements while enhancing the receptive field and convergence speed of control filters, a multiple-input batch processing strategy is utilized in pre-training. Simulations based on the common ESC-50 noise dataset are performed and demonstrate the superiorities of the proposed method in terms of classification accuracy, convergence speed, and steady-state noise cancellation.